Online and mobile advertisers want to know as much as possible about online and mobile device users so as to prosecute effective and sometime narrowly-targeted advertising campaigns. The Internet online advertising ecosystem has evolved to where a significant amount of personally identifiable information (e.g., email address, phone number, etc.) and non-personally identifiable information (e.g., gender, age, buying interests, etc.) about online users are collected on a daily basis. However, sets of personally identifiable information and sets of non-personally identifiable information from the same online user are not always associated or “matched”, and advertising opportunities are missed. For example, an advertiser that only knows some personally identifiable information (e.g., email address) of a user, would not necessarily be able to target that user against all attributes in the non-personally identifiable information profile of that same user. Further, privacy regulatory organizations have also established rules regarding treatment of personally identifiable information. For example, severe restrictions often exist with regards to data that includes personally identifiable information (e.g., data that can be associated to specific individuals), as opposed to non-personally identifiable information (e.g., data collected in an anonymous or non-personally identifiable information environment such as mobile browsing, online browsing, physical-location capture, etc.).
Legacy approaches have data aggregators working with owners of offline personally identifiable information (e.g., purchase data, coupon use data, customer relationship management (CRM) data, etc.) to match non-personally identifiable information data to the personally identifiable information. However, such legacy approaches involve the handling of personally identifiable information and such legacy approaches raise compliance and privacy issues, limiting the usefulness of such legacy techniques. Further, due, in part, to privacy compliance requirements and reliance on offline data, legacy matching approaches rely on offline data that is handled in a batch file exchange process. It can take several days or even weeks to complete a match cycle using offline data.
Therefore, there is a need for improved approaches to addresses the aforementioned problems.